In this paper, we consider the conditional value-at-risk (CVaR; also known as average
value-at-risk, tail conditional expectation, or expected shortfall) as an objective.
CVaR is a risk measure that quantifies the tail risk. Given a quantile value \(q \in (0,1]\) 
and a random variable that represents the cost, the CVaR value at level \(q\)  is defined as the
average of the worst \(q\) -fraction of the outcomes, i.e., the tail average beyond the \(q^\text{th}\) 
quantile of the cost distribution. Smaller values of \(q\)  focus on performance in the worst cases,
i.e., the trader is more risk averse, while \(q=1\) , on the other hand, corresponds to a risk
neutral trader. Starting from the pioneering work of [1]}, CVaR has
received much attention for its intuitive definition and for nice mathematical properties as a
convex and coherent risk measure.
